Optimizing Agricultural Planting Strategies under Dynamic Constraints: A Simulated Annealing-Based Multi-Scenario Approach
This study addresses the critical challenge of optimizing agricultural planting strategies in resource-constrained rural environments through a simulated annealing algorithm-based model. With increasing pressure on arable land due to agricultural modernization and population growth, we develop a comprehensive optimization framework integrating land heterogeneity, climatic constraints, and market dynamics to maximize economic returns. Our approach systematically addresses three critical scenarios: stable conditions requiring dual oversupply strategies (waste versus 50% discount sales), dynamic uncertainties involving demand fluctuations (±5–10%), yield volatility (±10%), cost inflation (5% annual), and price trends, and complex crop interdependence modeled through substitutability matrices and complementarity effects. Key innovations include adaptive simulated annealing with perturbation mechanisms, stochastic parameter handlers for climate-market volatility, and correlation-adjusted pricing models. Results demonstrate that the discounted oversupply strategy outperforms waste scenarios by 22.1% in profits, while legume rotation cycles reduce fertilizer dependency by 31% and boost subsequent crop yields by 10.2%. Validation confirms robustness against ±15% parameter shocks, with 92% convergence to global optimum across 54 land plots. Practical implementation in Hebei Province (2025) achieved a 28.5% profit increase, establishing this framework as a replicable solution for sustainable agricultural planning under uncertainty.
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